Long-COVID refers to a variety of symptoms affecting different organs reported by people following Coronavirus Disease 2019 (COVID-19) infection. To date, there have been no robust estimates of the incidence and co-occurrence of long-COVID features, their relationship to age, sex, or severity of infection, and the extent to which they are specific to COVID-19. The aim of this study is to address these issues.We conducted a retrospective cohort study based on linked electronic health records (EHRs) data from 81 million patients including 273,618 COVID-19 survivors. The incidence and co-occurrence within 6 months and in the 3 to 6 months after COVID-19 diagnosis were calculated for 9 core features of long-COVID . Their co-occurrence network was also analyzed. Comparison with a propensity score–matched cohort of patients diagnosed with influenza during the same time period was achieved using Kaplan–Meier analysis and the Cox proportional hazard model. The incidence of atopic dermatitis was used as a negative control.p < 0.001), co-occurred more commonly, and formed a more interconnected network. Significant differences in incidence and co-occurrence were associated with sex, age, and illness severity. Besides the limitations inherent to EHR data, limitations of this study include that (i) the findings do not generalize to patients who have had COVID-19 but were not diagnosed, nor to patients who do not seek or receive medical attention when experiencing symptoms of long-COVID; (ii) the findings say nothing about the persistence of the clinical features; and (iii) the difference between cohorts might be affected by one cohort seeking or receiving more medical attention for their symptoms.Among COVID-19 survivors , 57.00% had one or more long-COVID feature recorded during the whole 6-month period , and 36.55% between 3 and 6 months. The incidence of each feature was: abnormal breathing , fatigue/malaise , chest/throat pain , headache , other pain , abdominal symptoms , myalgia , cognitive symptoms , and anxiety/depression . All 9 features were more frequently reported after COVID-19 than after influenza  than after influenza.We do not know about the extent to which different features of long-COVID co-occur.This research used data from electronic health records of 273,618 patients diagnosed with COVID-19 and estimated the risk of having long-COVID features in the 6 months after a diagnosis of COVID-19. It compared the risk of long-COVID features in different groups within the population and also compared the risk to that after influenza.The research found that over 1 in 3 patients had one or more features of long-COVID recorded between 3 and 6 months after a diagnosis of COVID-19. This was significantly higher than after influenza.For 2 in 5 of the patients who had long-COVID features in the 3- to 6-month period, they had no record of any such feature in the previous 3 months.The risk of long-COVID features was higher in patients who had more severe COVID-19 illness, and slightly higher among females and young adults. White and non-white patients were equally affected.Knowing the risk of long-COVID features helps in planning the relevant healthcare service provision.The fact that the risk is higher after COVID-19 than after influenza suggests that their origin might, in part, directly involve infection with SARS-CoV-2 and is not just a general consequence of viral infection. This might help in developing effective treatments against long-COVID.The findings in the subgroups, and the fact that the majority of patients who have features of long-COVID in the 3- to 6-month period already had symptoms in the first 3 months, may help in identifying those at greatest risk. There is increasing concern, and emerging evidence, that some people who contract Coronavirus Disease 2019 (COVID-19) do not make a rapid or full recovery—so-called “long-COVID” or “long-haulers” . The terAlthough several studies have addressed the issue of long-term sequelae of COVID-19, they all have some important limitations. For example, the results of a telephone survey in France (with a 57% response rate reaching 478 patients) showed that at 4 months after hospitalization for COVID-19, about half the patients had at least one feature of long-COVID . In an aSome investigators have compared long-term sequelae of COVID-19 to those occurring after influenza. A 6-month retrospective cohort study based on electronic health records (EHRs) of 236,000 COVID-19 patients found higher rates of anxiety and mood disorders, insomnia, and dementia post-COVID-19 than after influenza . AnotherThe study protocol (not included in the supporting information), including cohorts definition, covariates, and outcomes, was planned before it was executed and almost exactly followed the protocol used in previous studies based on the same dataset (except for the outcomes that are specific to this study) ,15. The The study used TriNetX Analytics, a federated network of linked EHRs recording anonymized data from 59 healthcare organizations (HCOs), primarily in the United States of America, totaling 81 million patients. Available data include demographics, diagnoses (using ICD-10 codes), and measurements . The HCOs in the network are a mixture of hospitals, primary care, and specialist providers, and they contribute data from uninsured and insured patients. The process by which the data is deidentified is attested to through a formal determination by a qualified expert as defined in Section §164.514(b)(1) of the HIPAA Privacy Rule. This formal determination by a qualified expert, refreshed in December 2020, supersedes TriNetX’s waiver from the Western Institutional Review Board (IRB). For further details about TriNetX, its data, provenance, and functionalities, see Supporting Methods A in The primary cohort was defined as all patients who had a confirmed diagnosis of COVID-19 (ICD-10 code U07.1). It was compared to a matched cohort of patients diagnosed with influenza (ICD-10 codes J09-J11) who did not have a diagnosis of COVID-19 or a positive test for COVID-19. Cohorts included all patients over the age of 10 who had the index event (COVID-19 or influenza) on or after January 20, 2020 (the date of the first recorded COVID-19 case in the USA) and who were still alive at the end of follow-up . We note that patients in the influenza cohort must have presented in order to have been diagnosed, and, hence, the cohort is likely enriched for those who had a more severe form of the illness. Further details on cohorts are provided in Supporting Methods B in A set of established and suspected risk factors for COVID-19 and for more severe COVID-19 illness was used –17: age,In the absence of consensus as to what constitutes long-COVID, we adopted a pragmatic approach that attempted to capture features common to most descriptions, and which accounted for the fact that some features are diagnoses whereas others are symptoms. The 9 clinical features were as follows:chest/throat pain;abnormal breathing;abdominal symptoms;fatigue/malaise;anxiety/depression;pain;headache;cognitive dysfunction; andmyalgia.We defined each clinical feature using all ICD-10 codes we considered to be encompassed by the term. For example, to capture “abdominal symptoms,” we included abdominal pain, change of bowel habit, and diarrhea. We included atopic dermatitis as a negative control . See SupThe main outcomes for the analysis were the 9 long-COVID features. Their incidence and co-occurrence were analyzed both over the whole period from 1 to 180 days after the index event and specifically over the “long” phase, i.e., 90 to 180 days post-diagnosis (see below). In Kaplan–Meier analysis, outcomes are only counted the first time they occur in the follow-up period. Thus, if a patient has a feature recorded once in the acute phase of the illness and again a few months later, the latter occurrence would not count toward the incidence at later stages of follow-up. This implies that, in the 1- to 180-day follow-up, ongoing incidence after 3 months represents emergence of new long-COVID features in an individual with no record of these features before. Results from the “long” phase, on the other hand, assess the overall incidence of long-COVID features in the 90 to 180 days post-diagnosis . Jointly analyzing the incidence in these 2 time windows allowed us to separately estimate the rate of first occurrences and the rate of recurrences of long-COVID features .The choice to start the “long” phase at 90 days was motivated by an emerging consensus that this corresponds to the longer phase of the illness . The choPropensity score 1:1 matching  . This was achieved using simple algebraic relations between these and the recorded incidences over the whole 1 to 180 days and the “long” 90- to 180-day period  were assessed using natural cubic splines fitted to the log-cumulative hazard . The absD), which is the probability of co-occurrence divided by the mean of individual incidences  age 46.3 [19.8] years, 55.6% female), as well as for the matched COVID-19 and influenza cohorts . Adequate propensity score matching (SMD ≤ 0.1) was achieved for all comparisons.A total of 273,618 patients with COVID-19 were identified, and 114,449 patients with influenza were available for matching. In the 6 months after COVID-19 diagnosis, 57.00% (95% CI 56.59 to 57.43) had at least one feature of long-COVID recorded. This value includes the incidence of features recorded in the acute as well as the later phase of the illness. In the 90- to 180-day “long” phase post-diagnosis, 36.55% had a long-COVID feature recorded (95% CI 36.18% to 36.94%). The incidence of individual features ranged from 3.24%  for myalgia, to 22.8%  for anxiety/depression.In the “long” phase, the rates of first occurrence versus recurrences varied between clinical features . Overallp < 0.001 for the whole 1- to 180-day period; and overall HR = 1.56; individual HRs between 1.36 and 1.97; all p < 0.001 for the “long” phase). Long-COVID features are thus more common after COVID-19, although 42.8% of patients with influenza also had one of these features recorded over the same period (including 29.7% during the “long” phase). In contrast, the HR for atopic dermatitis (negative control) was not statistically significantly different from 1 . The absolute risk increase of any long-COVID feature after COVID-19 compared to influenza was 16.60% (95% CI: 15.84% to 17.33%) for the whole follow-up window and 12.64% (11.73% to 13.57%) for the “long” phase and varied between 1.43% and 8.71% (0.78% to 4.97% in the “long” phase) for different features . During the 90- to 180-day window, the clinical feature network in the COVID-19 cohort was similarly interconnected as in the influenza cohort .The clinical feature network was found to be more interconnected post-COVID-19 than following influenza  had significantly more features overall, and significantly more cognitive difficulties, but were less likely to have myalgia or headaches. There were no significant differences in the clinical feature networks in any of these subgroup comparisons , pain (p = 0.26), and myalgia (p = 0.77).Finally, among people who had a given long-COVID feature reported at least once, the total number of recorded occurrences of that feature was significantly larger among patients with COVID-19 than patients with influenza  recorded after COVID-19 was 34.2%  gives insight into the pattern of occurrences and recurrences of long-COVID features throughout the first 180 days post-COVID-19. Specifically, some features  are more common in the first 90 days post-COVID than in the next 90 days, whereas others  are more common in the “long” phase . It can We note several limitations, beyond those inherent to research using EHRs ,26 summ. First, The last limitation deserves further consideration for it might artificially increase the HR between cohorts. But 2 factors suggest that any effect is likely to be limited. First, the 2 cohorts were diagnosed with atopic dermatitis (a negative control) at the same rate over the same time period. Second, at the time our data collection ended (December 2020), long-COVID was far less in the public consciousness than it is now Click here for additional data file.S1 FiguresFig A. Kaplan–Meier curves showing the emergence of long-COVID features over 6 months. All symptoms of long-COVID are more common after COVID-19 (pink) than after influenza (blue). COVID-19 and influenza cohorts are propensity score matched for known COVID-19 risk factors. Shaded areas around curves represent the 95% CI. Fig B. Time-varying HRs for the 12 outcomes (out of 46) for which there was evidence of nonproportionality of hazards in the main comparison between the cohort of patients with COVID-19 and a matched cohort of patients with influenza. Shaded area represents a 95% CI. Fig C. Time-varying HRs for the 11 outcomes (out of 46) for which there was evidence of nonproportionality of hazards in the comparison between the cohort of patients with COVID-19 and a matched cohort of patients with influenza when the time window for follow-up is set to 3–6 months. Note that the x-axis is shifted on these figures so that day 1 corresponds to 3 months post-index event. Shaded area represents a 95% CI. Fig D. Using Dice’s coefficients and permutation testing, the long-COVID symptom network is seen to be more interconnected in the 6 months after COVID-19 than after influenza, after controlling for the incidence of each symptom in the network . The top panels represent the network as a graph with edges weighted by the values of Dice’s coefficient. The bottom panels provide the actual values of Dice’s coefficients for each pair of features. Fig E. Similar figure as Fig F. Using Dice’s coefficients and permutation testing, the long-COVID symptom network is seen to not be more interconnected in the 3 to 6 months after COVID-19 than after influenza, after controlling for the incidence of each symptom in the network . The top panels represent the network as a graph with edges weighted by the values of Dice’s coefficient. The bottom panels provide the actual values of Dice’s coefficients for each pair of features. Fig G. Kaplan–Meier curves for the comparison of the incidence of each and any clinical feature of long-COVID in the 6 months after a diagnosis of COVID-19 comparing matched cohorts of females (pink curves) vs. males (blue curves). Shaded areas represent 95% CIs. Fig H. HR for the individual incidence  and the co-occurrence  of clinical features of long-COVID after a diagnosis of COVID-19 comparing matched cohorts of females vs. males . Higher values are shown by intensity of red and blue shading. Only HR reaching significance at p < 0.05 are displayed, and the corresponding p-values are presented on the right panel . Fig I. Clinical feature networks after a diagnosis of COVID-19 among matched cohorts of females and males. Dice’s coefficients graphically represented as the edges of the networks in the top panels are reported numerically in the bottom panels. Fig J. Kaplan–Meier curves for the comparison of the incidence of each and any clinical feature of long-COVID in the 6 months after a diagnosis of COVID-19 comparing matched cohorts of non-white (pink curves) vs. white (blue curves). Shaded areas represent 95% CIs. Fig K. HR for the individual incidence  and the co-occurrence  of clinical features of long-COVID after a diagnosis of COVID-19 comparing matched cohorts of non-white vs. white . Higher values are shown by intensity of red and blue shading. Only HR reaching significance at p < 0.05 are displayed, and the corresponding p-values are presented on the right panel . Fig L. Clinical feature networks after a diagnosis of COVID-19 among matched cohorts of non-white and white patients. Dice’s coefficients graphically represented as the edges of the networks in the top panels are reported numerically in the bottom panels. Fig M. Kaplan–Meier curves for the comparison of the incidence of each and any clinical feature of long-COVID in the 6 months after a diagnosis of COVID-19 comparing matched cohorts of patients age 45 and over (pink curves) vs. age 10–44 (blue curves). Shaded areas represent 95% CIs. Fig N. HR for the individual incidence  and the co-occurrence  of clinical features of long-COVID after a diagnosis of COVID-19 comparing matched cohorts of patients age 45 and over vs. patients age 10–44 . Higher values are shown by intensity of red and blue shading. Only HR reaching significance at p < 0.05 are displayed, and the corresponding p-values are presented on the right panel . Fig O. Clinical feature networks after a diagnosis of COVID-19 among matched cohorts of patients age 45 and over vs. patients age 10–44. Dice’s coefficients graphically represented as the edges of the networks in the top panels are reported numerically in the bottom panels. Fig P. Kaplan–Meier curves for the comparison of the incidence of each and any clinical feature of long-COVID in the 6 months after a diagnosis of COVID-19 comparing matched cohorts of patients age 65 and over (pink curves) vs. age 45–64 (blue curves). Shaded areas represent 95% CIs. Fig Q. HR for the individual incidence  and the co-occurrence  of clinical features of long-COVID after a diagnosis of COVID-19 comparing matched cohorts of patients age 65 and over vs. patients age 45–64 . Higher values are shown by intensity of red and blue shading. Only HR reaching significance at p < 0.05 are displayed, and the corresponding p-values are presented on the right panel . Fig R. Clinical feature networks after a diagnosis of COVID-19 among matched cohorts of patients age 65 and over vs. patients age 45–64. Dice’s coefficients graphically represented as the edges of the networks in the top panels are reported numerically in the bottom panels. Fig S. Kaplan–Meier curves for the comparison of the incidence of each and any clinical feature of long-COVID in the 6 months after a diagnosis of COVID-19 comparing matched cohorts of patients age 22–44 (pink curves) vs. age 10–21 (blue curves). Shaded areas represent 95% CIs. Fig T. HR for the individual incidence  and the co-occurrence  of clinical features of long-COVID after a diagnosis of COVID-19 comparing matched cohorts of patients age 22–44 vs. patients age 10–21 . Higher values are shown by intensity of red and blue shading. Only HR reaching significance at p < 0.05 are displayed, and the corresponding p-values are presented on the right panel . Fig U. Clinical feature networks after a diagnosis of COVID-19 among matched cohorts of patients age 22–44 vs. patients age 10–21. Dice’s coefficients graphically represented as the edges of the networks in the top panels are reported numerically in the bottom panels. Fig V. Kaplan–Meier curves for the comparison of the incidence of each and any clinical feature of long-COVID in the 6 months after a diagnosis of COVID-19 comparing matched cohorts of patients requiring (pink curves) vs. not requiring hospitalization (blue curves). Shaded areas represent 95% CIs. Fig W. HR for the individual incidence  and the co-occurrence  of clinical features of long-COVID after a diagnosis of COVID-19 comparing matched cohorts of patients requiring vs. not requiring hospitalization . Higher values are shown by intensity of red and blue shading. Only HR reaching significance at p < 0.05 are displayed, and the corresponding p-values are presented on the right panel . Fig X. Clinical feature networks after a diagnosis of COVID-19 among matched cohorts of patients requiring vs. not requiring hospitalization. Dice’s coefficients graphically represented as the edges of the networks in the top panels are reported numerically in the bottom panels. Fig Y. Kaplan–Meier curves for the comparison of the incidence of each and any clinical feature of long-COVID in the 6 months after a diagnosis of COVID-19 comparing matched cohorts of patients requiring (pink curves) vs. not requiring ITU admission (blue curves). Shaded areas represent 95% CIs. Fig Z. HR for the individual incidence  and the co-occurrence  of clinical features of long-COVID after a diagnosis of COVID-19 comparing matched cohorts of patients requiring vs. not requiring ITU admission . Higher values are shown by intensity of red and blue shading. Only HR reaching significance at p < 0.05 are displayed, and the corresponding p-values are presented on the right panel . Fig AA. Clinical feature networks after a diagnosis of COVID-19 among matched cohorts of patients requiring vs. not requiring ITU admission. Dice’s coefficients graphically represented as the edges of the networks in the top panels are reported numerically in the bottom panels. Fig AB. Kaplan–Meier curves for the comparison of the incidence of each and any clinical feature of long-COVID in the 6 months after a diagnosis of COVID-19 comparing matched cohorts of patients with (pink curves) vs. without leukocytosis (blue curves). Shaded areas represent 95% CIs. Fig AC. HR for the individual incidence  and the co-occurrence  of clinical features of long-COVID after a diagnosis of COVID-19 comparing matched cohorts of patients with vs. without leukocytosis . Higher values are shown by intensity of red and blue shading. Only HR reaching significance at p < 0.05 are displayed, and the corresponding p-values are presented on the right panel . Fig AD. Clinical feature networks after a diagnosis of COVID-19 among matched cohorts of patients with vs. without leukocytosis. Dice’s coefficients graphically represented as the edges of the networks in the top panels are reported numerically in the bottom panels. Fig AE. HRs comparing the incidence of clinical features of long-COVID between matched subgroups of patients diagnosed with COVID-19. The figure includes the spider plots shown in p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05, n.s. p > 0.05. Fig AF. Number of references on medRxiv containing the term “long-COVID” in 2-month interval since the beginning of the pandemic. This shows that the study  largely took place at a time where public awareness long-COVID was significantly less than now. This suggests that public awareness alone is unlikely to have led to substantially more patients seeking medical attention for otherwise equal symptoms as the control cohort. CI, confidence interval; COVID-19, Coronavirus Disease 2019; HR, hazard ratio; ITU, intensive treatment unit.(DOCX)Click here for additional data file.S1 TablesTable A. Characteristics of the unmatched COVID-19 cohort and the matched COVID-19 and influenza cohorts. Table B. Contributions of incidence (within 6 months of a diagnosis of COVID-19 vs. influenza) of subcategories making up the clinical features of long-COVID in matched cohorts. Table C. Incidence of long-COVID features in the whole cohort of patients with COVID-19 within the entire follow-up period (0–6 months), the first half of the follow-up period (0–3 months), and the second half of the follow-up period (3–6 months). In the analysis of the 3–6-month follow-up, those who had the long-COVID feature recorded in the first 3 months and then again in the next 3 months were included so that the sum of the incidences in the two-halves of the follow-up window exceeds the total incidence. Table D. Absolute risk increase in COVID-19 vs. influenza (a positive number indicates a higher risk in COVID-19) in the whole 0–6-month period as well as the “long” phase (3–6 months). Table E. 95% CIs corresponding to the entries in Table F. 95% CIs corresponding to the entries in Table G. 95% CIs corresponding to the entries in p-values were <0.0001 except for the co-occurrence of cognitive symptoms and myalgia (p = 0.0007). Table H. 95% CIs corresponding to the entries in p-values were <0.01 except for the co-occurrence of myalgia and headache (p = 0.1476), myalgia and cognitive symptoms (p = 0.1292), and myalgia and pain (p = 0.0139). Table I. p-values for the test of proportional hazards  for the main analysis (1 day to 6 months follow-up) and the analysis restricted to the 3 months–6 months follow-up. A value lower than 0.05 indicates evidence for nonproportional hazards. Table J. Average degrees of the clinical feature networks in the different comparisons between cohorts. p-values were obtained using permutation tests. Table K. 6-month incidence of individual long-COVID features and of any feature in different subgroups of patients  diagnosed with COVID-19. Table L. 6-month incidence of individual long-COVID features and of any feature in different subgroups of patients defined by indices of severity of COVID-19 illness. Table M. Characteristics of the female and male COVID-19 cohorts after propensity score matching. Table N. Characteristics of the non-white and white COVID-19 cohorts after propensity score matching. Table O. Characteristics of the age 45+ and age 10–44 COVID-19 cohorts after propensity score matching. Table P. Characteristics of the age 65+ and age 45–64 COVID-19 cohorts after propensity score matching. Table Q. Characteristics of the age 22–44 and age 10–21 COVID-19 cohorts after propensity score matching. Table R. Characteristics of COVID-19 cohorts requiring and not requiring hospitalization, after propensity score matching. Table S. Characteristics of COVID-19 cohorts requiring and not requiring ITU admission, after propensity score matching. Table T. Characteristics of leukocytosis and non-leukocytosis COVID-19 cohorts after propensity score matching. Table U. Mean count number of occurrences of each and any long-COVID feature among patients who have them recorded at least once, in the 6 months after a diagnosis of COVID-19 or influenza (using matched cohorts). The p-value tests the hypothesis that the counts are equal between the cohorts. Table V. Comparison in the 6-month incidence of any pain, between patients with COVID-19 and a matched cohort of patients with influenza. Any pain in this analysis refers to the composite endpoint of chest/throat pain, headache, myalgia, other pain (as defined in Supporting Methods D) or abdominal and pelvic pain . CI, confidence interval; COVID-19, Coronavirus Disease 2019; HR, hazard ratio; ITU, intensive treatment unit; SMD, standardized mean difference.(DOCX)Click here for additional data file.S1 TextSupporting Methods A. TriNetX network. Supporting Methods B. Definition of cohorts. Supporting Methods C. Definition of covariates. Supporting Methods D. Definition of outcomes. Supporting Methods E. Details on statistical analyses. Supporting Methods F. Details on secondary analyses.(DOCX)Click here for additional data file.


L-arginine showed significantly reduced levels at the finish line (p < 0.05) but was normalized one week after the race. Markers of endothelial inflammation E-Selectin, VCAM-1, and ICAM-1 all showed a pattern with increased values at the finish line compared to before the race , with normalization one week after the race. In conclusion, we found acutely reduced FMD with reduced L-arginine levels and increased E-Selectin, VCAM-1, and ICAM-1 immediately after the Norseman Xtreme triathlon. Our findings indicate a transient reduced endothelial function, measured by the FMD-response, after prolonged strenuous exercise that could be explained by reduced NO-precursor L-arginine levels and increased endothelial inflammation.We assessed endothelial function by flow-mediated dilatation (FMD), levels of the NO-precursor L-arginine, and markers of endothelial inflammation before, at the finish line, and one week after the Norseman Xtreme triathlon. The race is an Ironman distance triathlon with a total elevation of 5200 m. Nine male participants were included. They completed the race in 14.5 (13.4–15.3) h. FMD was significantly reduced to 3.1 (2.1–5.0)% dilatation compared to 8.7 (8.2–9.3)% dilatation before the race ( Flow-mediated dilatation (FMD) refers to the non-invasive assessment of endothelial function by studying the brachial artery’s dilatation in response to increased blood flow . The dilThere are conflicting reports on the acute changes of FMD after exercise ,14,15,16Prolonged strenuous exercise, as in Ironman distance triathlons, is known to cause a multitude of physiological changes that may influence the endothelial function, including a state of inflammation ,19,20, iWe hypothesized that prolonged strenuous exercise affects the endothelium’s inflammatory state and L-arginine’s bioavailability, a precursor of the NO-signaling molecule . To asseThe study was conducted on participants in Norseman Extreme Triathlon 2019. The competition started with a jump from a ferry and a 3800 m swim in the Hardanger Fjord, followed by 180 km bicycling, with an elevation of more than 3000 m before finishing off with a 42.2 km run to Mt. Gaustatoppen (1883 m above sea level). The total elevation of the racecourse was 5200 m.The study was announced through an email to participants, and interested participants were sent more information about the study before they decided to enroll. The day before the race start, willing male participants who could perform a follow-up measurement at Oslo University Hospital one week after the race were included in the study. Male participants were selected because we could not control for menstrual cycles due to fixed race dates . The parThe Regional Committee for Medical and Health Research Ethics in Norway (REC) approved all experimental measurements (reference: 2016/932), and the study was conducted according to the Declaration of Helsinki. All participants gave written informed consent.Participants were examined in the supine position after 20 min of rest. A standard blood pressure cuff was positioned around the right arm, two inches below the antecubital fossa, and the brachial artery was imaged 5–9 cm above the ante-cubital fossa. A linear array transducer probe  was used to acquire the brachial artery’s images. The transducer was placed in a fixed position at the brachial artery and stabilized with a custom-made tripod. The cuff was inflated to a supra-systolic pressure (230 mmHg) for 5 min. B-mode images and pulsed doppler measurements of the right brachial artery were continuously captured for 190 s, starting 10 s before the cuff was deflated. Automated analysis of end-diastolic diameters and blood velocities was performed in an edge-detecting software . FMD was calculated as the percentage increase in diameter from 10 s before cuff deflation to maximum dilatation post-occlusion. The maximum flow was calculated from blood velocities and diameter measurement as a measurement of stimuli.g for 10 min within 1 h, and the serum was pipetted to separate freeze-tolerant containers. All samples were transported on ice to freezing storage with a temperature of −80 °C. Commercial ELISA kits were used for VCAM-1, ICAM-1, and E-selectin . Intra assay coefficients of variations (CVs) were 2.1%, 2.8%, and 2.2%, respectively. L-arginine was determined by high-performance liquid chromatography (HPLC) and pre-column derivatization with o-phthaldialdehyde (OPA) .Venous blood samples were collected at all time points in vacuum containers with silica particles and gel separators. The blood was clotted for 30 min at room temperature, serum was separated by centrifugation at 2000× p-value of <0.05 was considered significant. All values are presented as median . Statics was conducted in R .Friedman’s tests were applied to examine for any differences between the times of measurements for each test. Wilcoxon Signed-Rank Sum tests were applied as post hoc tests. A Nine male participants of the Norseman Xtreme Triathlon were included in this study. Complete sets of blood samples were collected in eight subjects. The subjects did not compete in any long-distance running or triathlon competition the week between the measurements. Characteristics of the subjects are given in p < 0.05). The FMD returned to baseline values one week after the competition . There were no significant differences between arterial baseline diameters (p = 0.24) or maximum flow calculations (p = 0.10) between the time points.As seen in p < 0.05) and reduction one week after the race. E-selectin returned to baseline values one week after the race (p = 0.74). VCAM-1 was reduced below baseline values one week after (p < 0.05). ICAM-1 showed a similar trend to VCAM-1, but levels after one week were not statistically significantly different from the baseline (p = 0.06). L-arginine showed significantly reduced levels at the finish line (p < 0.05) and was normalized one week after the race (p = 0.08). Full results for E-Selectin, VCAM-1, ICAM-1, and L-arginine are shown in E-Selectin, VCAM-1, and ICAM-1 all showed a similar pattern with increased values at the finish line compared to before the race  is 2.9 μM for purified eNOS [m of 29 ± 6 μM calculated by extracellular L-arginine levels effect on intracellular eNOS activity [m for eNOS [Reduced circulating L-arginine levels could offer a mechanistic explanation for the reduced FMD-response upon completion of the race. Vascular endothelial cells synthesize NO from L-arginine by NO synthase (NOS) ,31, predied eNOS , which iactivity . Assuminactivity ,40 to imactivity ,42,43 anactivity . All of activity ,42,43,44for eNOS . There aIn addition to increased endothelial inflammation and reduced L-arginine levels, other possible mechanisms not evaluated in the present study might reduce FMD post-exercise. These include increased sympathetic activity ,46 and vOne week after the race, we found VCAM-1 to be slightly reduced compared to baseline, while there was no significant change between baseline and the measurement one week after for FMD, ICAM-1, E-selectin, and L-arginine. We do not have a biochemical answer for the reduced VCAM-1 levels one week after the race. There is no international reference for VCAM-1, but the producer of the ELISA kits reports a standard range of 350–990 ng/mL for VCAM-1, and the levels one week after the race were within these limits. A limitation in the present study is the relatively small sample size, making it possible that there were other minor changes after the race that we did not find statistically significant due to a lack of statistical power. However, the more considerable changes found immediately after the race was not present one week later.The significantly reduced endothelial function with increased endothelial inflammation and reduced L-arginine bioavailability was transient. Therefore, there was no evidence of any long-lasting reduced endothelial function after an Ironman triathlon from our study. However, it was previously proposed that there may be a U-shaped effect of exercise on endothelial function over time. It was proposed that moderate to high levels of exercise have a beneficial effect on endothelial function while both lack of exercise and extreme levels of exercise cause impaired endothelial function in otherwise healthy individuals . SupportThe reduced FMD and L-arginine levels found immediately after completing the Norseman Xtreme triathlon were normalized one week after. In parallel, there was an altered endothelial inflammation that might in part explain the altered endothelial function.


Apical periodontitis (AP) is a common oral disease caused by the inflammatory destruction of the periapical tissues due to the infection of the root canal system of the tooth. It also contributes to systemic bacterial translocation, where peripheric mononuclear blood cells (PBMCs) can act as carriers. Toll-like receptor (TLR) 2 mediates the response to infection and activates inflammatory responses. DNA methylation can be induced by bacteria and contributes to the modulation of this response. Despite the evidence that supports the participation of PBMCs in immune-inflammatory disorders, the inflammatory profile and epigenetic regulatory mechanisms of PBMCs in AP individuals are unknown.To determine TLR2 gene methylation and inflammatory profiles of PBMCs in AP.Cross-sectional exploratory study. Otherwise, healthy individuals with AP (n=27) and controls (n=30) were included. PMBCs were isolated by a Ficoll gradient, cultured for 24 hours, and both RNA and DNA were extracted. DNA was bisulfite-treated, and specific sites at the promoter region of the TLR2 gene were amplified by qPCR using validated primers. To verify its amplification, agarose gels were performed. Then, the PCR product was sequenced. mRNA expression of TLR2 was determined by qPCR. The soluble levels of 105 inflammatory mediators were first explored with Proteome Profiler Human Cytokine Array Kit. Consequently, tumor necrosis factor (TNF)-α, interleukin (IL)-6, IL-10, IL-6Rα, IL-1β, and IL-12p70 levels were measured by Multiplex assay.PBMCs from individuals with AP demonstrated a proinflammatory profile showing higher soluble levels of TNF-α, IL-6, and IL-1β compared to controls (p<0.05). Higher TLR2 expression and higher global methylation pattern of the promoter region of the gene were found in AP compared to controls (p<0.05). The CpGs single-sites at positions -166 and -146 were completely methylated, while the site -102 was totally unmethylated, independently of the presence of AP. DNA methylation of CpG single-sites in positions -77 and +24 was positively associated with TLR2 expression.PBMCs from AP subjects show a hyperinflammatory phenotype and TLR2 upregulation in association with single CpG-sites’ methylation from the TLR2 gene promoter, thereby contributing to a sustained systemic inflammatory load in individuals with periapical endodontic diseases. Apical periodontitis (AP) is among the main causes of tooth loss and is typified by the inflammatory destruction of the periapical tissues as a consequence of the infection of the root canal system of the tooth . Its halPorphyromonas (P.) gingivalis and P. endodontalis, can trigger the TLR2 pathway (κ-B (NFκB) translocation to the nucleus and the consequent inflammatory cascade activation  play a pivotal role. TLRs are a family of receptors activated by damage-associated molecular patterns (DAMPs) and pathogen-associated molecular patterns (PAMPs) . Among t pathway , 8. The tivation . The exptivation .Epigenetics can regulate the activation of the immune cell in response to infection by transcriptional reprogramming , 12. TheP. endodontalis, can invade the peri radicular tissues and translocate to distant sites within peripheric blood mononuclear cells (PBMCs)  . In thisognition , and reaognition –23, the This analytic cross-sectional exploratory study was conducted under the approval of the Ethics-Scientific Committee of the Central Metropolitan Health Service (Number 2017/70) and from the Faculty of Dentistry, Universidad de Chile (Number 2016/08). All the volunteers were informed about the objectives and procedures of the study and signed the informed consent. All procedures followed the ethical standards of the institutional and/or national research committees and were under the 1964 Helsinki declaration and its later amendments.2, and antibiotic and/or anti-inflammatory drug consumption three months before the study , a periodontist (P.H.), an endodontist (M.G). The ALEO size was determined as the mean of vertical and horizontal diameters (mm). Full-mouth radiographic evaluations were obtained. Intraoral parameters including periodontal probing depth (PPD) and clinical attachment levels (CAL) were determined with a periodontal probe  in six sites per tooth. All the enrolled patients received hygiene instruction, full-mouth ultrasonic scaling, and coronal prophylaxis before entering the study. The subjects with AP diagnosis were properly treated (M.G) after the sample collection.®) according to the manufacturer’s instructions.Fasting blood samples were obtained from AP n=27) and healthy controls volunteers (n=30). The samples were collected by venipuncture of the antecubital vein by a qualified technician . Briefly, samples were mixed with a cocktail of biotinylated detection antibodies fixed on a membrane-based sandwich immunoassay, according to the manufacturer’s instructions. Three independent samples per group were loaded in duplicates. The captured proteins were visualized by chemiluminescence and semi quantification was performed by densitometric scanning . The intensity was determined by the mean pixel density of the duplicate spots that represent each cytokine. The negative control was the clear area of the membrane. Also, the levels of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, IL-6Rα, IL-12p70, and IL-10 were measured by Multiplex assay  using a Luminex platform , following the manufacturers’ recommendations. The data were analyzed by the MILLIPLEX AnalystR software .Isolated PBMCs from individuals with AP as well as controls were cultured in a 96-well plate in a 2 x 10μl).DNA was purified from PBMCs using the DNeasy isolation kit  and RNA was extracted with TRIzol Reagent (Invitrogen™), following the respective manufacturer’s instructions. The quality and concentration of DNA and RNA were quantified using a spectrophotometer  in the ratio of 260:280. A reverse transcription kit  was used for synthesizing the single-stranded cDNA from the RNA samples (8 ® FAST qPCR Kits (KAPA Biosystems), using the forward sequence 5’-CTCTCGGTGTCGGAATGTC-3’ and the reverse sequence 3’-AGGATCAGCAGGAACAGAGC-5’, as previously reported  with validated primer set for TLR2 promoters CpG island: forward sequence 5’-TAAGGGCGGGAGTTTGTTGGGAAGTAC-3’ and reverse sequence 3’-CTCCGAACCGACCTACCCGAAACTAAA-5’, as previously reported (http://www.ncbi.nlm.nih.gov/nuccore/NC_000004.12?report=genbank&from=153684256&to=15370609 by Methprimer program (http://www.urogene.org/cgi-bin/methprimer/methprimer.cgi). The PCR amplified product integrity was determined on agarose gels (2%) and submitted to Sanger sequencing service . The sequencing results were analyzed with the BiQ Analyzer program and expressed as the relative frequency of global methylation  and single-CpG methylated sites within the CpG island from the TLR2 gene promoter.Purified DNA (200 ng) from each sample was bisulfite-treated . Bisulfited DNA was amplified by KAPA SYBRreported . The selt-test or Mann-Whitney U-test according to data distribution. Spearman’s correlation was used to evaluate the association between quantitative variables. The comparison of categorical variables was performed with the chi-square test. To explore the associations between TLR2 global and CpG single-site’s methylation, as independent variables, and TLR2 mRNA expression as the outcome variable independently of potential confounders, we ran linear bivariate and multiple regression models using backward stepwise regression, removing the covariates with p ≥ 0.2. To explore the influence of periapical inflammation and other potential exposure factors on the global methylation of the TLR2 gene promoter´s CpG island, we performed bivariate fractional regression for potential confounders and then fit the models including those covariates with significant p values (p<0.1). To facilitate interpretation, we estimated and presented the marginal effects. Statistical analyses were performed with Stata statistical package 12 . All figures were constructed on GraphPad Prism 9 software .Data distribution of numerical variables was assessed by the Shapiro-Wilk test. Comparisons between AP and controls were analyzed by ) composed of 17 up-regulated and 7 down-regulated inflammatory mediators was determined in AP versus controls (p<0.05). Also, the concentrations (pg/mL) of inflammatory cytokines commonly associated with systemic inflammation were determined . On the other hand, the levels of IL-6Rα, IL-12p70, and IL-10 did not show differences between the study groups .General, intraoral, and serum parameters of enrolled volunteers are shown in Analysis of TLR2 expression revealed significantly higher mRNA expression levels in AP subjects compared to the control group . The global methylation status alone did not associate with TLR2 transcriptional activity (p=0.42). At the site level, CpG methylated positions at -77 and +24 were positively associated with TLR2 transcriptional activity in bivariate models  and multivariate model , and the later model explained 33% of the variability.To further understand the influence of the TLR2 promoter’s methylation status over its transcriptional activity, linear regression modeling was performed Table 2Table 2Influence of the TLR2 promoter’s methylation status in gene expression.Finally, potential modifying factors of TLR2 promoter’s gene methylation were explored Table 3.AP has largely been considered as a local infection that causes the inflammation and disruption of the periapical tissues, though recent evidence demonstrates that ALEOs also contribute to the systemic bacterial and inflammatory burden, acting as a non-classic risk factor for non-communicable diseases , 18. In α, IL-1β, and IL-6 in comparison with healthy controls. Despite there being no reports in AP, PBMCs from individuals with periodontitis have also shown a proinflammatory profile with enhanced secretion of interferon-γ, IL-6, IL-12p70, IL-17, and monocyte chemoattractant protein-1 versus their healthy counterparts  or methyl-CpG-binding proteins in a CpG site-specific manner –54. Partcription . In our cription . The meccription .A few studies sustain that aging is associated with global hypermethylation of CpG islands . In our Concluding, PBMCs show a hyperinflammatory phenotype and TLR2 upregulation in AP, which in turn, is influenced by the methylation of single CpG-sites from the TLR2 gene promoter, contributing to a sustained systemic inflammatory load in individuals with periapical endodontic diseases.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by the Ethics-Scientific Committee of the Central Metropolitan Health Service (N 2017/70) and from the Faculty of Dentistry, Universidad de Chile (N2016/08). The patients/participants provided their written informed consent to participate in this study.MH, MG, AC, and PG-H: conceptualization and design. MH, MB, and ML: data curation. MH: Funding acquisition. MH, AF, and MB: Formal analysis. MB, AF, and JA: laboratory experiments. MH: Project administration. MB and MH: design the figures. MH: Resources. MH: Software. MG, MH, and PH: Supervision. MB, AF, and MH: Writing manuscript. All authors critically reviewed and approved the manuscript.This study was funded by FONDECYT grants 1160741 and 1200098 from ANID, the Chilean government. MB is a recipient of the scholarship ANID 21210551, from the Chilean Government. AF is a recipient of the scholarship CONICYT 21181377, from the Chilean Government. MG is a recipient of the scholarship ANID 21210509, from the Chilean Government.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In the training group, five immune-related methylated CG sites  were identified, and CG site-based risk scores were calculated using univariate Cox proportional hazards regression in patients with stage II/III CRC. Multivariate Cox regression analysis indicated that methylated signature was independent of other clinical parameters. The Kaplan–Meier analysis results showed that CG site-based risk scores could significantly help distinguish between high- and low-risk patients in both the training (P = 0.000296) and test groups (P = 0.022). The area under the receiver operating characteristic curve in the training and test groups were estimated to be 0.771 and 0.724, respectively, for prognosis prediction. Finally, stratified analysis results suggested the remarkable prognostic value of CG site-based risk scores in CRC subtypes. We identified five methylated CG sites that could be used as an efficient overall survival (OS)-related biomarker for stage II/III CRC patients.With the increasing incidence of colorectal cancer (CRC) and continued difficulty in treating it using immunotherapy, there is an urgent need to identify an effective immune-related biomarker associated with the survival and prognosis of patients with this disease. DNA methylation plays an essential role in maintaining cellular function, and changes in methylation patterns may contribute to the development of autoimmunity, aging, and cancer. In this study, we aimed to identify a novel immune-related methylated signature to aid in predicting the prognosis of patients with CRC. We investigated DNA methylation patterns in patients with stage II/III CRC using datasets from The cancer genome atlas (TCGA). Overall, 182 patients were randomly divided into training ( In China, colorectal cancer (CRC) is the fifth most common malignancy, and CRC-related deaths have increased in recent years . ApproxiAberrant DNA methylation results in the downregulation of various genes and can potentially initiate the pathogenesis of cancer. It is a promising candidate for the development of robust diagnostic, predictive, and prognostic biomarkers for cancer. For instance, hypomethylation of long interspersed nuclear element-1 is correlated with poor survival in CRC patients . AdditioIn this study, we aimed to identify and validate a novel immune-related methylated site-based signature using CRC datasets from the cancer genome atlas (TCGA). Based on our results, we proposed a prognosis-related biomarker that is also effective for patients with CRC subtypes.1 (n = 127) and testing groups (n = 55) . The expression data of GSE103479 was based on GPL23985 Platforms included 363 stage II/III CRC samples .We downloaded the epigenome-wide DNA CpG site methylation scored as a β-value between 0 and 1 (Illumina 450 K Methylation Beadchip) of stage II/III CRC samples from the Genomic Data Commons data portal1 . Overall(n = 55) . We obta(n = 55) . The “MaP < 0.05 as thresholds. Thereafter, we focused on the upregulated methylated sites between CRC and adjacent normal tissues and mapped them to immune genes. Overall, 2483 immune genes were downloaded from the immunology database and analysis portal (ImmPort)3. Finally, through this analysis, we identified 349 upregulated immune-related methylated sites in stage II/III CRC samples  between stage II/III CRC and adjacent normal tissues were identified using the edgeR package, with | log2 FC| > 1.0 and adjusted  samples .P < 0.1). We used a stepwise selection algorithm for selecting signatures to construct a reliable and an efficient predictive prognostic model  combinations of the nine CG sites. For each combination, the CG site-based risk score (CG Score) was calculated based on the following equation, where N is the number of methylated sites of the signature, Methi is the methylation value of the candidate sites, and Coefi is the univariate Cox regression coefficient:Machine learning algorithms for predictive models have been described previously . First, ic model . There aWe calculated the CG Score for each sample and the median CG Score in the training group was used as the cut-off value (cut-off = 0.67). Next, we divided all samples into high- and low-risk groups. The Kaplan-Meier survival method, as well as the log-rank test, was applied to compare the prognosis between two groups. In this study, we used area under the curve (AUC) as the performance measurement method for predictive models, which was plotted using the “survivalROC” R package, and all statistical tests were performed using R-3.6.3.We calculated relative percent of 22 immune cells in each sample by CIBERSORT which included gene expression of 22 leukocyte subtypes . Then weP < 0.1)  . Next, sP < 0.1) . The CG n = 64) and low-risk groups (n = 63), and the OS in the low-risk group was higher than that in the high-risk group in the training group  and low-risk (n = 20) groups, and the OS in the low-risk group was higher than that in the high-risk group in the test group  demonstrated that CG Score was independent of other clinical characteristics both in the training and test groups . In addiPrevious research showed immune checkpoint genes including PDL1, interferon-gamma (IFN-γ), PDL2, CTLA4, etc. We found negative correlations between CG Score and other markers , which i4. There is different for TIDE score between high and low CG Score (t-test p = 0.067) and the AUC for CG Score under 5 and 3 years are 0.771 and 0.699. In addition, the AUC for TIDE under 5 and 3 years are 0.599 and 0.551 (Tumor immune dysfunction and exclusion (TIDE) is a gene expression biomarker developed for predicting the clinical response to immune checkpoint blockade . We obtand 0.551 . The resSCTR, PIK3CD, FGF5, PLXNC1, and LTBP4. We observed that the high expression levels of PIK3CD, PLXNC1, and LTBP4 were correlated with the MSI and TMB-H groups  to assist in verifying the prognostic value of CG Score, and found that gene model can show good prognostic ability in independent verification datasets   .A recent study reported that approximately 30% of CRC patients experience tumor recurrence in the first 3 years after surgery and adjuvant chemotherapy . There iin vitro culture experiments show that the high expression of ICAM-1 depends on the increased dose of IFN-γ and IL-1β in CRC tissues was significantly increased and positively correlated with the infiltration of inflammatory cells in the tumor microenvironment. The results of and IL-1β, For theand IL-1β , there are no chemotherapeutics Drugs for the methylation signature. The most sensitive drugs targeting PI3K-Akt signaling pathway (PIK3CA-D) are Alpelisib and Taselisib. If future studies find a drug suitable for the CpG site, experiments can be conducted to verify the sensitivity of the drug.The original contributions presented in the study are included in the article/GW, YF, and FL conceived and designed the study. FC, LP, EM, and XW acquired the data and drafted the manuscript. SH, DW, YL, and XH analyzed and interpreted the data. XH and SL critically revised the manuscript for important intellectual content. SL, FL, GW, and YF approved the version of the manuscript to be published. All authors contributed to the manuscript and approved the submitted version.SL, XW, EM, SH, and DW were employed by ChosenMed Technology (Beijing) Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To investigate the effects of insulin resistance (IR) on IVF outcomes and a potential underlying mechanism in lean women without PCOS.A prospective cohort study at the University Clinic.IVF center at the University setting.A total of 155 lean women (body mass index <25) without PCOS undergoing IVF cycle.120.Patients were allocated to IR and non-IR groups based on HOMA-M3 (25-OH-VD3) levels were analyzed.IVF outcomes, including egg quality, the percentage of mature oocytes, fertilization rate, blastocyst formation rate, advanced embryo rate, and cumulative live birth rate were investigated. Auto-immune parameters, peripheral blood immunophenotypes, thyroid hormone, homocysteine, and 25-OH-vitamin D+ B cells was significantly higher in the IR group than those of the non-IR group (p<0.05). Homocysteine, 25-OH-VD3, and auto-immune parameters were the same between the two groups.The percentage of mature oocytes and blastocyst formation rate were significantly lower in the IR group as compared with those of the non-IR group . The proportion of peripheral blood CD19In lean infertile women without PCOS, IR is associated with the decreased percentage of mature eggs and poor embryo quality in which B cell immunity may play a role. Lean non-PCOS women with insulin resistance have adverse IVF outcomes with the decreased percentage of mature oocytes and blastocyst formation rate and increased peripheral blood B cell levels.Insulin resistance (IR) is typically defined as decreased sensitivity or responsiveness to the metabolic action of insulin . IR is cHyperinsulinemia disrupts the intrafollicular microenvironment during folliculogenesis and reduces the rate of fertilization and embryonic development potential during the natural and ovarian stimulation cycles , 8. InsuThere is increasing evidence at the cellular level showing that inflammation is a critical factor for obesity-induced IR. The tissue-resident immune cells, especially adipose-tissue resident cells, play a major role in regulating obesity-induced inflammation. On the other hand, cellular and molecular factors in adipose tissue regulate obesity-induced inflammation and IR . ElevateObesity is a global epidemic related to numerous health concerns, including reproductive disorders. Central obesity is considered an independent risk factor for early miscarriage . Obese i120 was reported as a reliable and straightforward measure of IR for lean European and Asian women with PCOS /22.5. IR was diagnosed when HOMA-M120>12.8. Lean women with HOMA-M120>12.8 were considered as IR group, and the others were non-IR group  as the index to assess IR. The HOMA index was calculated as HOMA = (fasting insulin uIU/ml * fasting glucose mmol/l)/22.5.HOMA-IR has been reported to effectively predict IR in the overweight-obese PCOS population with a cutoff of 2.62 or more (AUC 84.1%) . Howeverith PCOS , 19. HOMIR group . PreviouIR group . But theThis was a prospective observational study done at Reproductive Medicine Center, Department of Obstetrics and Gynecology, the First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC, Hefei, Anhui, China, from May 2016 to July 2018. Ethics approval was given by the Ethical Committee of the First Affiliated Hospital of USTC, and all study candidates signed the informed consent form prior to entering the study.Inclusion criteria were couples with only tubal factor infertility, women’s age from 18 to 40 years old and all underwent a standard long-protocol agonist IVF cycle as the first treatment cycle. Tubal etiology was diagnosed if hysterosalpingography or laparoscopy showed evidence of bilateral tubal obstruction.Exclusion criteria were women with polycystic ovarian syndrome (PCOS) according to the Rotterdam criteria, high BMI (>25), male infertility, and other endocrine and systemic diseases(including type 1 or 2 diabetes), without familial diabetes history. Patients undergoing ICSI for fertilization failure were also excluded.A total of 190 women were asked to participant, but only 170 women agreed to participant and were subjected to an oral glucose tolerance test (OGTT) before the IVF cycle. 5 participants were excluded from the study for taking metformin before the cycle which is known to affect glucose metabolism and insulin sensitivity. Four cases were lost during the follow-up. 6 women were excluded for fertilization failure.Finally, a total of 155 lean infertile women were included in the study. The flow chart is shown in 2) level and B ultrasound scan. Once the criteria for desensitization were satisfied , Patients was daily injected 150-225 IU of recombinant FSH . Injection of recombinant hCG  250μg were given when diameter of 2-3 follicle ≥17 mm or at least diameter of one follicle ≥18 mm. Oocytes retrievals were performed 36 h after hCG administration. All patients received conventional IVF as fertilization method regardless of their ages.Before the IVF cycle, the BMI, hormonal, biochemical, immunological, and ultrasound parameters were investigated. All underwent a standard long-protocol agonist IVF cycle. Based on the BMI of patients, 0.9-1.5mg Triptorelin was initiated on day 21 to achieve adequate ovarian suppression. After 14 days, pituitary desensitization was checked by estradiol /22.5. IR was diagnosed when HOMA-M120>12.8. Lean women with HOMA-M120>12.8 were considered as IR group (n=58), and the others were non-IR group (n=97) , 2-h blood glucose (2-h BG), and 2-h insulin levels were measured. HOMA-Mp (n=97) . OHSS ha2, follicle-stimulating hormone (FSH), luteinizing hormone (LH), total testosterone (T) and prolactin (PRL) were done during cycle day 2-4 of the menstrual cycle. The oral glucose tolerance test (OGTT) was performed at the same time after overnight fasting and the administration of an oral hypertonic glucose solution (75g). Serum glucose and insulin were measured at 0 and 120 min. Body mass index (BMI) was calculated according to the formula, weight/height2. Serum E2, FSH, LH, T and PRL were measured employing commercial RIA kits .The serum glucose level was determined by an automatic analyzer employing an enzymatic-colorimetric assay . Insulin level was measured by RIA using a commercial kit , and homocysteine (HCY) level was determined using an enzyme conversion immunoassay kit . The 25-OH-VD3 level was measured using the liquid chromatography/tandem mass spectrometry (LC/MS/MS) 155 method at the reference laboratory. Thyroid-stimulating hormone (TSH) and free thyroxine (FT4) were measured by electrochemiluminescence immunoassay . The cut-off values for the reference range was 0.4–4.0 mIU/L for TSH and 11–25pmol/L for FT4 (All baseline blood sampling including E for FT4 . Subclin for FT4 .Antiphospholipid antibodies (APA) and anti-β2GP1 were tested by enzyme-linked immunosorbent assay (ELISA). Anti-nuclear antibodies (ANA) were performed by indirect immunofluorescence using a commercially available kit . Anti-thyroglobulin antibody (ATG) and anti-thyroperoxidase (Anti-TPO) were tested by ELISA .+ cells, NK cells as CD3-CD16+CD56+ cells, and B cells as CD19+ cells.Peripheral blood immune effectors were analyzed by the flow cytometry analysis. Briefly, whole blood samples were labeled using fluorochrome-conjugated monoclonal antibodies (mAb) against CD45-PC5, CD3-FITC, and CD56-PE, or CD45-PC5 and CD19-FITC , and samples were analyzed by a FC 500 flow cytometer using CXP software (Beckman Coulter). Lymphocytes were gated based on side scatter characteristics and CD45 expression. Within lymphocytes, T cells were identified as CD3Statistical analyses were performed using the Statistical Package for the Social Sciences software . Student’s t-test and chi-square test were applied to determine the differences between the means or the distributions of the study groups. Results were presented as mean ± SD for each group, and p-value <0.05 was considered to be significant.120 index, 58 women had IR (37.4%), and 97 women had normal insulin sensitivity (62.6%). Women with IR had a significantly higher incidence of subclinical hypothyroidism . However, age, BMI, antral follicle count, and baseline hormone levels, including FSH, LH, E2, PRL, and T, were similar between IR and non-IR groups. As expected, FBG, 2h BG, FI, 2-h insulin, HOMA-IR were significantly different between the two groups. There were no differences in HCY  and 25-OH-VD3  levels in women with IR as compared with those of the non-IR group (Based on the OGTT and HOMA-M+ B cells in women with IR (12.73 ± 4.37%) was significantly higher than that of women with non-IR groups   and NK cell  populations in the peripheral blood were not different between the two groups.There was no significant difference in auto-immune parameters between IR and non-IR groups, including ATA, ATG, APA, ANA, and a-β2GP1 ,the percentage of mature oocytes per oocytes retrieved , and freezable blastocysts per residual embryos  were significantly different between the two study groups. However, there were no differences in the number of retrieved oocytes , the incidence of Ovarian Hyperstimulation Syndrome , the number of fertilized oocytes , fertilization rate , the percentage of fresh ET cycles in total ET cycles and the percentage of good-quality embryos per cleaved embryos . In non-IR group, 14 patients were diagnosed with mild OHSS and 2 patients with moderate OHSS. In IR group, 10 patients were diagnosed with mild OHSS and 2 patients with moderate OHSS. No severe or critical OHSS happened in either non-IR group or IR group. The small sample size limited the stratification analysis. But we found there was no difference in the incidence of mild OHSS .In the IR group, the duration of induction ,pregnancy rate , chemical pregnancy loss rate,clinical pregnancy loss rate  and pre-term births rate  between IR and non-IR group were not different.Besides, an ectopic pregnancy occurred in non-IR group in the first ET cycle. Moreover, cumulative live birth rate per oocyte retrieval cycle  was not different between the two study groups  and d-chiro-inositol (DCI) are two inositol stereoisomers in humans. MYO is the precursor of inositol triphosphate, a second messenger that regulates thyroid-stimulating hormone (TSH) and FSH as well as insulin. Several preliminary studies suggest that a deficiency of D-chiro-inositol (DCI) containing IPG might be at the basis of insulin resistance . In factFinally, in contrast to the non-IR group, the IR group had a significantly higher level of peripheral blood B lymphocytes. Since there is no difference in autoimmunity between the IR and non-IR groups, increased B cells in the IR group may reflect the expansion of the functional subset of B cells. Previous study found B cells can worsen glucose tolerance by production of IgG antibodies and activation of proinflammatory macrophages and T cells in mice model. Depletion of B cells in mice can ameliorate glucose tolerance and fasting insulin. However, the return of B cells will exert their detrimental effects on glucose tolerance again. These results indicates B cells play a role in insulin resistance . In our Previously, we reported that women with low vitamin D levels had higher peripheral blood B-cell proportion and T helper/T cytotoxic cell ratios than those of normal vitamin D . In thisThe study is limited since the underlying mechanism of higher B lymphocyte count in the peripheral blood of women with IR was not explored further and auto-antibodies to insulin and its receptors were not investigated. Since the genetic study of the embryos were not made in all cases, IR and genetic abnormalities of the embryos were not thoroughly investigated.Currently, the independent role of IR in IVF outcomes is not defined well , 36. OurThe original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.The studies involving human participants were reviewed and approved by the Ethical Committee of the First Affiliated Hospital of USTC. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.All authors qualify for authorship by contributing substantially to this article. HW and LW were responsible for study design and conception, data analysis, the first draft of the article, review, and approval of revisions, and the final article. JK-K interpreted the data and revised the manuscript. XF and YZ were responsible for recruitment, data collection, and article review. All authors contributed to the article and approved the submitted version.This study was supported by the National Natural Science Foundation .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Patients and Methods: This retrospective, population-based study analyzed the clinical data of 162 DTC patients from signal institution in China and 26,487 cases from the Surveillance, Epidemiology, and End Results (SEER) program registry. The patients were divided into two groups  in the SEER cohort. Furthermore, patients were grouped as follows: T4; N1b; M1; T1-3N0-1a; specific number of lymph node metastases; and total or near-total thyroidectomy. Results: The 120-month cause-specific survival (CSS) rate of women and men showed a gradual declining trend from 60–64 to ≥80 years of age in the group that underwent surgery. The CSS rate of women and men showed a marked downward and irregular trend with an increase in age in the recommended, but no surgery group in the SEER cohort. Univariate analysis indicated that the surgery group had a higher 120-month CSS in women in most stages and men, compared with the no surgery group in the SEER cohort. The analysis of the SEER cohort showed that RAI-131 therapy was associated with an improved 80-month CSS in T4/N1b/M1 women (P < 0.0183) and men (P < 0.0011). However, there were no CSS differences between the RAI-131 therapy and the no-RAI-131 group for the patients with T4/N1b/M1 (AJCC 7th) thyroid cancer in the Chinese cohort. There was no CSS difference in women or men between the T1-3N0 and T1-3N1a patients in the SEER cohort. And similar findings were observed in T1-3N1a patients in the Chinese cohort. There was no statistical difference between the two subgroups. Conclusions: Surgical treatment should be recommended for elderly DTC patients because surgery can lead to a better CSS. High-risk patients achieve a higher benefit-to-risk ratio with RAI-131 therapy. To avoid the adverse effects associated with RAI-131 therapy, a multidisciplinary discussion should be arranged for intermediate- and low-risk patients.Purpose: The purpose of the current study was to determine whether older patients with differentiated thyroid cancer (DTC) who received surgical treatment had a better cause-specific survival (CSS) than patients who were recommended surgery, but declined, and whether patients who underwent postoperative RAI-131 therapy had an impact on CSS based on TNM staging and number of lymph node metastases for all total or near-total thyroidectomy patients.  Thyroid cancer is the most frequent endocrine malignancy and the incidence has nearly tripled in the past few decades in the US . Among tSurgery is the preferred treatment for most patients with differentiated thyroid cancer (DTC), which plays an important role in the prognosis of the disease. Previous studies showed that the strategy of total thyroidectomy with subsequent levothyroxine treatment could decreased the risk of both recurrence and mortality in patients with high-risk DTC without initial distant metastases . In thisPostoperative disease status should be taken into consideration when deciding whether to recommend additional treatment. In general, postoperative radioactive iodine-131 (RAI-131) adjuvant therapy is routinely recommended for DTC patients at high risk based on the American Thyroid Association (ATA) guidelines. RAI-131 therapy has been used for patients for the following indications: remnant ablation; potential micrometastases; locoregional invasion; and pulmonary or bone metastatic disease. The purpose of therapy is to ensure a tumoricidal effect , 6. TreaWe sought to determine the outcomes between patients who underwent surgery and patients in whom surgery was recommended but declined. We also determined the outcomes between elderly patients with DTC who did and did not receive postoperative RAI-131 therapy.∗Stat software . All data regarding demographic characteristics and cancer incidence were obtained from the SEER∗Stat 8.3.6 program of incident cases from 1975–2016 (November 2018 submission) [The SEER data were derived from the SEER database (November 2016 submission) using SEERPatients who met the following criteria were selected . Only paFurthermore, we also aimed to identify whether postoperative RAI-131 therapy had an impact on CSS with different TNM stage and the number of positive lymph nodes. Thus, we divided patients with T4, N1b, M1, T1-3N0-1a, and the number of metastatic lymph nodes who underwent a total or near-total thyroidectomy.We retrospectively collected the clinical data of 583 DTC patients who received thyroidectomy at Sichuan Cancer Hospital between January 1, 2000, and September 31, 2011. All procedures were followed by the ethical standards of the responsible committee on human experimentation  and with the Helsinki Declaration of 1964 and later versions. All patients received standard follow-up, including laboratory and clinical examinations after discharge from the hospital every 3 months for the first 3 years, every 6 months during the fourth and fifth years, and once a year thereafter until the patient died or until the date of last follow-up (September 2020). Three doctors in each medical center are responsible for follow-up and recording of patients' information.The outcomes of interest included thyroid CSS and duration of survival for men and women. Duration of survival was defined as the time from the date of diagnosis to the date of death or last contact. Cause of death was defined using the SEER cause of death recode. Patients who died not due to thyroid carcinoma were designated as CSS while patients diagnosed with thyroid carcinoma who were still alive as overall survival.χ2 test or Fisher's exact test were used to compare means and proportions, respectively, between categorical variables and treatment groups. The Kaplan Meier estimator was used to determine thyroid cancer CSS and plot survival curves. Cox proportional hazards regression was used to determine hazard ratios (HRs) with 95% confidence intervals (CIs). All statistical tests were two-sided and statistical significance was defined as a P < 0.05.Mean and interquartile ranges were reported for continuous variables. Frequency and proportion were reported for categorical variables. Pearson Overall, in the SEER cohort, 26,487 elderly patients (≥60 years of age) diagnosed with DTC were identified; 26,049 patients underwent surgical treatment and 438 were recommended to undergo surgery, but surgery was not performed. The mean age at the time of diagnosis was 73.50 (SD ± 7.07) years, 68.97 (SD ± 6.90) years in the group that had surgery, and 76 (SD ± 8.64) years in the group that was recommended to have surgery but did not undergo surgery . After ath American Joint Committee on Cancer (AJCC)) patients did and did not receive RAI-131 therapy, respectively. Of 2548 patients, 223 (not T1-3N0) underwent total or subtotal thyroidectomy and 209 and 132 T1-3N1a patients underwent total or subtotal thyroidectomy, respectively. In these cohorts, the mean ages were 69.52 (SD ± 7.18) versus 71.87 (SD ± 8.25) years, 67.90 (SD ± 6.11) versus 67.91 (SD ± 6.36) years, and 67.88 (SD ± 6.64) versus 67.77 (SD ± 6.54) years between patients who did or did not receive RAI-131 therapy, respectively  and men (95.54%–79.86%) showed a gradual declining trend from patients 60–64 to ≥ 80 years of age and stage in the group that had surgery in the SEER cohort. The CSS rate of women (90.68%–52.00%) and men (94.74%–16.22%) showed a marked downward and irregular trend with an increased age in the group recommended to have surgery, but who did not have surgery. And in the Chinese cohort, the 120-month CSS rate of women (92.86%–69.33%) and men (92.31%–71.59%) also showed a gradual declining trend from patients aged 60–64 to patients aged older than 80.Univariate analysis indicated that the surgery group in the SEER cohort had a higher 120-month CSS among women in most stages and men, compared with the group that did not have surgery Figures .P < 0.018) and men  and T1-3N1a female and male patients  women  thyroid cancer  during the past four decades, based on improved knowledge of the biology of DTC and on advances in therapy, including surgery, the use of radioactive iodine (radioiodine), thyroid hormone treatment, and availability of recombinant human TSH . SurgeryAge has been identified as a well-recognized prognostic determinant of CSS in older patients, although DTC is relatively indolent. Age has a linear dose-dependent relationship with the DTC mortality rate. An age-specific cutoff corresponding to a marked decrement in survival is not apparent. Therefore, DTC patients have a higher cancer-specific mortality rate in elderly patients, rather than the tumor becoming more inactive as age increases , 13. AnoThe 2015 ATA guidelines suggest modifications in risk stratification for DTC patients. The consensus guidelines recommend that RAI-131 should be offered to high-risk patients to reduce the risk of disease recurrence or mortality for DTC patients after a total or near-total thyroidectomy ; howeverThe updated guidelines have supported both decreased RAI doses for select populations, as well as expanded definitions for low- and intermediate-risk patients that may not require RAI-131. A growing body of literature indicates that the benefit-to-risk ratio is necessary to accurately identify patients who are ideal candidates for the therapy. RAI-131 can avoid unnecessary treatment, cost, and adverse effects at the same time , 20, 21.th AJCC) patients who received RAI-131 therapy had a better CSS than patients who did not receive RAI-131 therapy. However, there was no difference in the number of PLNs  among T1-3N0-1a patients (7th AJCC), which are stage I or II (8th AJCC) in the SEER cohort. There was no difference in T1-3N0-1a patients with 0, 1–3, and >3 PLNs. And the similar finding was observed in the T1-3N1a DTC patients in the Chinese cohort. Therefore, the patients at high risk have a higher benefit-to-risk ratio by RAI-131 therapy. Otherwise, the intermediate- or low-risk patients have fewer benefits from RAI therapy, which is similar with the patients only stimulating hormone-suppressive therapy and without RAI-131 therapy. And the patients who did not receive RAI therapy could avoid the associated risks of adverse effects.The debate centers on the question of appropriate use of postoperative adjuvant RAI therapy. T4/N1b/M1 (7The endocrinologist and nuclear medicine physician have an indispensable role in RAI-131 decision-making, the role of surgeons has been shown to exert substantial indirect influence on RAI-131 use, and the treatment concept is often a pivotal factor in postoperative management, especially for intermediate- and low-risk patients. Such patterns, however, may lead to inappropriate use of RAI-131. The main aim of this study was to selectively leverage the strengths of RAI-131 therapy and determine whether use of RAI-131 is suitable for high-risk patients who benefit, as well as intermediate- and lower-risk patients who may not benefit based on a multidisciplinary, risk-adapted approach .There were several limitations to the current study. This was a nonrandomized observational study with the possibility of selection bias and inherent coding errors, especially an inability to confirm histological status in patients who did not undergo surgery. The SEER database did not capture key information variables, such as the largest size of metastatic lymph nodes, extranodal extension, vascular invasion, the histologic subtype, genetic mutations, specific RAI-131 dose, and other tumor or nodal factors that may have influenced on the decision to administer RAI-131 treatment, cancer recurrence, and prognosis outcomes. The number of patients recommended to undergo surgery, but who did not have surgery was small, and the TNM status of most patients was blank, so we cannot further analyze the influence of different TNM staging on CSS.In conclusion, our analysis indicated that surgical treatment should be recommended to elderly DTC patients because elderly DTC patients treated with surgery can achieve a better CSS. The high-risk patients have a greater benefit-to-risk ratio through RAI-131 therapy. To avoid the related adverse effects associated with RAI-131 therapy, a multidisciplinary discussion about treatment options depending on the balance between clinical benefit and treatment-related toxicity should take place for intermediate- and low-risk patients.


Both finite element method and analytical solution based on continuum theory provide qualitatively consistent results for the curling behavior, reflecting that relaxation of intrinsic strain induced by the atomic asymmetry acts as the driving force. The non-edge bandgap of curled A-MoSSeNR reduces faster with the increase of width compared with planar nanoribbons. It can be observed that the real-space wave function at the non-edge VBM is localized in the central region of the curled nanoribbon. When the curvature is larger than 1.0 nm−1, both edge bandgap and non-edge bandgap shrink with the further increase of curvature. Moreover, we explore the spontaneous curling and consequent sewing process of nanoribbon to form nanotube (Z-MoSSeNT) by MD simulations. The spontaneously formed Z-MoSSeNT with 5.6 nm radius possesses the lowest energy. When radius is smaller than 0.9 nm, the bandgap of Z-MoSSeNT drops rapidly as the radius decreases. We expect the theoretical results can help build the foundation for novel nanoscale devices based on Janus TMD nanoribbons.Based on density functional theory, we theoretically investigate the electronic structures of free-standing armchair Janus MoSSe nanoribbons (A-MoSSeNR) with width up to 25.5 nm. The equilibrium structures of nanoribbons with spontaneous curling are obtained by energy minimization in molecular dynamics (MD). The curvature is 0.178 nm Lu et al. stripped the top layer of S atoms with hydrogen plasma and substituted them with hydrogen atoms, and the subsequent thermal selenization led to the replacement of H atoms by Se to form stable monolayer of MoSSe . In addiic layer , dipole ic layer , electroic layer ,11,12,13ic layer ,15, phonic layer , piezoelic layer  and vallic layer ,19. Owinic layer , photocaic layer ,23,24,25ic layer ,30,31,32ic layer , moleculic layer  and so oIn addition to the 2D Janus TMDs, its one-dimensional (1D) derivatives, such as nanoribbons , are reg2 nanoribbons [2 nanotubes is several orders of magnitude larger than that obtained in planar 2D WS2 monolayers [Experimentally, tightly scrolled TMD nanotube can be produced from monolayer TMD flakes with assistance of ethanol solution . In contoribbons . Meanwhioribbons ,44. Theooribbons ,46,47,48nolayers .Although efforts have been made for theoretical investigations on properties of Janus TMD nanoribbons and nanotubes, the structures considered in literature (below 120 atoms) were not large enough to demonstrate a full picture of curling behavior and spatial localization of carriers ,45,47,48−6 eV for energy and 0.01 eV/Å for forces on each atom. The optimized lattice constants, heights of Mo-S layer (Mo-Sh) and Mo-Se layer (Mo-Seh) in Janus MoSSe monolayer are 3.228 Å, 1.533 Å and 1.708 Å respectively, which are in agreement with MD simulation results [Firstly, we perform geometry optimization of Janus MoSSe primitive cell using Vienna ab initio simulation package  with the projector augmented wave (PAW) potentials ,51. Perd results .−10 eV/Å. The curled nanoribbons obtained from structural relaxation are taken as equilibrium structures in the following DFT calculations. Moreover, to clearly capture the spontaneous curling and consequent sewing process of large nanoribbons to form nanotubes, a 35.19 nm wide planar nanoribbon is relaxed under NVT ensemble  for 5 ns (with the time step of 1 fs) at 0.1 K [The primitive cell of Janus MoSSe monolayer is expanded along zigzag direction, and periodic condition is applied in the armchair direction to construct Janus armchair MoSSe nanoribbons (A-MoSSeNRs). The two edges of the nanoribbons are ensured symmetrical by deleting redundant atoms. The top and side view of A-MoSSeNR geometries are shown in at 0.1 K . At high−5 Hartree and 10−5 e, respectively.The first-principles calculations of armchair-oriented nanoribbons and nanotubes are carried out by Real space Electronic Structure Calculator , which is a powerful computational solver based on Kohn-Sham density functional theory with Chebyshev filtering . The gen−1, as measured in the transition metal atomic layer. Interestingly, this value is almost constant at different places in one nanoribbon and regardless of nanoribbon width (marked as W). In this sense, the curling property of A-MoSSeNR, such as curvature, is mainly determined by the material properties. As a validation, we perform a geometry optimization of a relatively narrow (20 periods) A-MoSSeNR in VASP. The curvature determined from DFT is 0.179 nm−1, showing good agreement with our MD result. To further explore the origin of spontaneous curling, we resort to FEM and analytical solution based on continuum elastic theory. The geometry of A-MoSSeNR is modeled by putting a MoS sublayer on a MoSe sublayer with simple 2D geometry while the atomic asymmetry in Janus structure is modeled by two equivalent misfit strains [i denotes the layer number from bottom to top, ih is the height of ith layer, iz is as marked in ith layer. For plain strain assumption, E = 299 GPa and −1 and this lower value comes from more flexible free edges in quantum dots for strain relaxation, compared with A-MoSSeNR.Initial free-standing planar A-MoSSeNRs are relaxed in MD simulations to obtain equilibrium structures based on energy minimization, as plotted in  strains . It shou ν=0.245  are Youn ν=0.245 . The curHere we investigate the electronic structure of sixteen A-MoSSeNRs, including eight planar and eight curled nanoribbons. The width ranges from 6.1 nm to 25.5 nm. Since our nanoribbon models are relatively large and we intentionally focus on the curling effect, the curled nanoribbons are directly exported from LAMMPS without further optimization on the edges. Consistent with previous studies , there a1 to V4) are localized on the left edge of nanoribbon, while the corresponding conduction band ones (C1 to C4) are localized on the right. If edges are passivated, the conductivity behavior would be dominated by the localization of wave functions at valence band maximum (VBM) and conduction band minimum (CBM) of non-edge-states. The partial charge densities at the top of the non-edge valence bands of A-MoS2NR with width less than 4 nm have been investigated [5) is delocalized over the whole planar A-MoSSeNR. As illustrated in 6) clearly shrinks to the central region. On the other hand, the profile of wave function at the bottom of non-edge conduction bands (C5) is basically identical to V5 in planar nanoribbon. However, the bottom conduction band (C6) wave function dissipates from the central region and is separated to lateral regions in nanoribbon after spontaneous curling.To further explore the spontaneous curling effect on the charge carriers of A-MoSSeNR, we resort to the real-space wave function. stigated . Consist−1, which represents the maximum curvature of such nanoribbon. The maximum curvature of 6.1 nm wide nanoribbon is thus set 0.892 nm−1. To demonstrate the curling effects under larger curvature, we chose nanoribbon with a smaller width of 2.9 nm, and the limit value of curvature is set 1.622 nm−1. −1. Both the energy of edge-states and non-edge-states shift to the Fermi level as the curvature further increases, reflecting that larger curvature reduces the bandgap. For non-edge-states, the shift of CBM under curling is smaller than that of VBM. As reported, the edge states shift down to the non-edge valence bands at a critical curvature [As discussed above, spontaneous curling slightly reduces the non-edge bandgap of A-MoSSeNR and affects its real-space wave function. The curvature role in its electronic properties needs to be further discussed. As shown in urvature ,62. Henc−1, we demonstrate that a 35.19 nm wide (109 unit cells in zigzag direction) A-MoSSeNR can spontaneously form a MoSSe nanotube (Z-MoSSeNT). In MD simulations, the initial flat nanoribbon is relaxed for 5 ns at temperature of 0.1 K. The snapshots of the spontaneous curling and consequent sewing process are illustrated in Finally, considering that the curvature of MoSSe nanoribbon is around 0.178 nm−1, the further increase of curvature will obviously shrink both edge and non-edge bandgaps of A-MoSSeNRs. Since the reconstruction of edge is not taken into consideration, the energy of edge-state is stable regardless of width and curvature. Moreover, we explore the spontaneous formation process of Z-MoSSeNT from A-MoSSeNR in MD simulations. The radius of this spontaneously formed nanotube is 5.6 nm, and it possesses the lowest energy compared with nanotubes with other curvatures. When the radius is smaller than 0.9 nm, the rapid reduction of Z-MoSSeNT bandgap can be observed with the decrease of radius.In summary, we investigate the impact of spontaneous curling on the electronic structures of large-scale free-standing A-MoSSeNRs. The non-edge bandgap of curled A-MoSSeNR varies from 1.67 eV to 1.45 eV with the increase of nanoribbon width up to 25.5 nm. Compared with planar nanoribbons, the real-space wave function at the non-edge VBM is localized in the central region. In addition, when the curvature is larger than 1.0 nm